Statistical Questions from CPV Monitoring of Bioreactor Data

Transcription

1 Statistical Questions from CPV Monitoring of Bioreactor Data Craig Bernier Principal Statistician Design to Value and Quality Engineering Janssen Pharmaceutical Companies of Johnson and Johnson

2 Individual Value CPV Statistics Assay Control Chart Example Trending of Typical CQA of a Product Much support around developing procedures, teaching control charts, and providing backup to more complex questions. As per the 20 Guidance: UCL=0.50 _ X=97.2 The data should be statistically trended and reviewed by trained personnel Observation LCL=92.92 We recommend that a statistician or person with adequate training in statistical process control techniques develop the data collection plan and statistical methods and procedures used in measuring and evaluating process stability and process capability.

3 Common Statistical Questions for CPV Data failed the Normality test Do I have to transform the Data? How should I transform the data? or What transformation do I use? What Shewhart tests to apply? Long Term vs Short Term sd Can I use Levey Jennings Chart? 2

4 Common Statistical Question for CPV For dissolution do I just chart the averages? Or Individuals? For individuals do I use: Individuals chart? X-bar / R, or X-bar / S? Should we use the I-MR R/S (Between / Within) charts? Do I have to calculate Process Capability? How often? Use Cpk or Ppk? Confidence intervals? 3

5 Statistically Related Questions If I see a point outside the control limits, do I have to open an investigation? The control limits are to 3 decimals, but the spec is one decimal? The data is rounded to same precision as the specification. Do I need to ask for unrounded data? 4

6 Somewhat Statistically Related Questions How often do I need to trend / report? Why? How to evaluate risks, prioritize actions from CPV reports? Do I need to trend Yields? Do I need to trend CMA (Critical Material Attributes)? CPP (Critical Process Parameters)? Is Minitab/JMP/R Validated? 5

7 More Complex Statistical Questions For Example: How to monitor longitudinal data from the bioreactor during CPV? Multiple CPV parameters (e.g. Viable Cell Density, Viability, IgG content, ph) Daily offline measurements for each batch Unique data profile across culture period Up to 60 days depending on product 6

8 Upstream Production Process Stage : Preculture and expansion Stage 2: Production by continuous perfusion in bioreactor continuous perfusion bioreactor 7

9 Bioreactor Data 8

10 Problem How to evaluate cell growth data over time for trending purposes. When is a batch acting differently than usual what tool to use to detect special cause variability?

11 Problem Continued Initial solution from our practitioners. This type of trending shows a sample average and some expected variability around that model. Essentially mean +/- 3sd at each day A very nice solution to be able to detect unusual results and potential special cause variability Some challenges though

12 Problem Continued We do not have the ability to monitor the process over time as we usually desire in CPV Is process trending up / down? Another challenge is that this approach allows for multiple chances for a batch to signal. What to do about a batch that has a single point or a couple points outside the range but in general follows a typical pattern

13 Alternate approach Trending of residuals from average at each day This approach captures the deviations from average across time as the current approach. But also allows us to then trend the batches over time potentially using typical control chart methods.

14 Plot Individuals Autocorrelation exists due to the nature of the data

15 StDev Mean Plot Averages and sd Time Series Plot of Residuals Mean Batch Batch 5 Batch 0 Batch 5 Batch 20 Batch 25 Batch Batch 30 Batch 35 Batch 40 Batch 45 Time Series Plot of StDev Batch Batch 5 Batch 0 Batch 5 Batch 20 Batch 25 Batch Batch 30 Batch 35 Batch 40 Batch 45

16 Sample StDev Individual Value Charting of Average Residuals I chart of Residuals Means UCL= _ X= LCL= Batch Batch 6 Batch Batch 6 Batch 2 Batch 26 Batch Batch 3 Batch 36 Batch 4 Batch 46 Batch 3 omitted from calculations S Chart of SRES UCL=.093 _ S= Batch Batch 6 Batch Batch 6 Batch 2 Batch 26 Batch Batch 3 Batch 36 Batch 4 Batch 46 LCL=0.567 *Standard X-bar chart shows variability more than within batch variability. We use I-MR R/S type approach

17 Batches of interest identified Control Charts signaled at Batch 2, Batch 2, and Batch 3. (as well as Batch 6)

18 Sample Mean Trend Data by Test Date Potential additional benefit of evaluating the residuals is that we could also plot the data by test date Xbar Chart of SRES by Test Date _Qtr 20xx UCL=0.586 Are test results unusually high / low on a particular day indicating some lab variability? _ X= LCL= Date

19 References Montgomery, Douglas C. Introduction to Statistical Quality Control 7th edition, Wiley, 203. *Note: Montgomery text page discusses various works with some examples on monitoring autocorrelated data as well as profile monitoring. 8

20 Questions Thanks to: Tim Overkleeft Wendy de Wit Kevin Pipkins Clemens Haerder David Enck